AbstractClinical trial designs often incorporate a sequential stopping rule to serve as a guide in the early termination of a study. When choosing a particular stopping rule, it is most common to examine frequentist operating characteristics such as type I error, statistical power, and precision of confidence intervals (Statist. Med. 2005, in revision). Increasingly, however, clinical trials are designed and analysed in the Bayesian paradigm. In this paper, we describe how the Bayesian operating characteristics of a particular stopping rule might be evaluated and communicated to the scientific community. In particular, we consider a choice of probability models and a family of prior distributions that allows concise presentation of Bayesian properties for a specified sampling plan. Copyright © 2006 John Wiley & Sons, Ltd.